Working with Data in R

Dataframes, Lists, External Files, Paths

Bogdan G. Popescu

bogdan.popescu@johncabot.edu

John Cabot University

What You’ll Learn Today

Lists & Vectors
Missing Data
Strings
Data Frames
Subsetting

Intro

Press CMD + A on a Mac to select everything

Press Ctrl + A on a Windows to select everything

Intro

Press “Delete” to delete everything

Intro

Now replace it with:

---
title: "Lab 2"
author: "Your Name"
date: "September 13, 2024"
format:
  html:
    toc: true
    number-sections: true
    colorlinks: true
    smooth-scroll: true
    embed-resources: true
---

Intro

This is how we save the document.

Make sure that you save it in the relevant folder.

Intro

This is how we render the qmd file into an HTML.

Intro

This is how we add a chunk of code.

This is where we only place R code.

Intro

Notice how the R code chunk starts

Intro

Notice how the R code chunk ends

Intro

This is what

“# Introduction”

and

“This is the introduction.”

look like when we render the document

Intro

This is how we run a chunk.

Intro

This is what our list looks like in the computer’s memory.

Intro

This is how we remove one item from the list and save a new list.

Intro

Output:

Logical Subsetting

This is how we can do logical subsetting

# Create a numeric vector
list_no <- c(11, 12, 13, 14, 15, 16, 17)

# Keep values less than or equal to 13 OR greater than or equal to 15
list_no_filtered <- list_no[list_no <= 13 | list_no >= 15]
list_no_filtered

[1] 11 12 13 15 16 17

Logical Subsetting

This is how we can do logical subsetting

# Create a character vector
list_words <- c("random", "word", "sentence", "books")

# List of words to exclude
exclusion_list <- c("word", "sentence", "books")

# Keep only elements not in the exclusion list
list_words_filtered <- list_words[!(list_words %in% exclusion_list)]
list_words_filtered

[1] "random"

Strings & Comparisons

This how we can check which string is “greater” alphabetically and how to count the number of characters in a word

# Check which string is "greater" alphabetically
"four" > "five"  # TRUE because "o" comes after "i" in the alphabet

[1] TRUE

# Count the number of characters in the word
nchar("four")

[1] 4

Missing Data

This is how we can handle missing data.

# Create a vector with a missing value (NA)
list_no <- c(1, 2, 3, 4, NA, 5, 6)

# Remove missing values using !is.na()
list_no_clean <- list_no[!is.na(list_no)]
list_no_clean

[1] 1 2 3 4 5 6

Mean & Median

This is how we calculate the mean and the median

# Create a numeric vector with missing values
list_no <- c(30, 12, NA, 14, NA)

# Compute mean and median while ignoring NAs
mean(list_no, na.rm = TRUE)

[1] 18.66667

median(list_no, na.rm = TRUE)

[1] 14

Creating a Dataframe

# Define student names and grades
students <- c("Alex", "Jane", "Tom", "Lilly", "Turner", "Ruby", "Nick")
grade <- c(77, 81, 89, 83, 99, 92, 97)

# Create a data frame from the vectors
df <- data.frame(student = students, grade = grade)
df

Creating a Dataframe

df is a dataframe

student and grade are variables within that dataframe

Creating a Dataframe

Subset rows for specific students

This is how we select specific observations

subset_df <- subset(df, student %in% c("Alex", "Jane", "Turner"))
subset_df

Creating a Dataframe

Subset rows for specific students

Creating a Dataframe

Subset rows for specific students

Creating a Dataframe